Modern day medical and biomolecular imaging scanners can generate large amounts of data in a short period of time, usually requiring a dedicated computer for processing and visualization. For indirect medical imaging modalities/devices, such as MRI, PET and CT, the raw data, commonly called k-space data, needs to be mathematically transformed into images which require super scale computing power. This process, called image reconstruction, can take hours using current desktop x86 or Macintosh systems and severely limits clinical use of medial imaging applications. Unfortunately, current solutions use a dedicated imaging processing system for a single modality, making inefficient use of computing resources.
Recent advances in multi-core computer processor technology will drastically reduce image processing time. It will also open the door to new possibilities of sharing computer intensive processors among the modalities. Emerging multi-core processors are able to accelerate medical imaging applications by exploiting the parallelism available in their algorithms. Unfortunately all existing systems require a separate processing system for each imaging device, which is both costly and decentralized.